Large companies and other organizations tend to rely on a networked information technology (IT) infrastructure to conduct day-to-day operations. In many cases, the IT infrastructure is extremely large and complex. Such IT enterprise infrastructures may include a bundled combination of services and hardware. In the case of a complex networked IT infrastructure, a service provider who installs and maintains the system could have procedures that allow recognition of service value only when services are actually completed. Moreover, complex network infrastructures could represent multiple element arrangements for certain business purposes. The service provider may only be able to recognize service value for each element in the networked IT infrastructure. In systems having a large number of elements, service value recognition may present a complex challenge for a service provider.
While pricing can vary among the elements, the pricing consistency is an important factor in achieving certain strategic business objectives. A service element (as compared to a hardware element) might have two components. Those two components are the service rate that is charged when the service is sold, and the number of hours needed to complete the service in the multiple-element arrangement.
While IT enterprises in general are expected to have the ability to establish a reasonably dependable estimate of the number of hours required to complete the services, there are some factors that significantly affect the service rates that the IT service providers would charge. One such factor is that labor rates could be negotiated in special arrangements by customers and IT service providers based on perceived value of the services. Another factor is that labor rates may be renegotiated if a project is extended or additional resources are required. Yet another factor is that different business units in an IT enterprise could in general have different market focuses, covering everything from off-site support, on-site repair-as-needed support, customized program and solutions management or the like. Labor rates, therefore, while essentially the same across the services for comparable skill levels, vary greatly depending on what skill level is needed. Yet another factor that can affect service rates is transaction volume. Service rates may increase for enterprises with relatively large transaction volumes.
Another complicating factor is that transaction volumes can be different across different regions of a common IT infrastructure. For example, a global enterprise may have a high transaction volume in a particular country and a relatively low transaction volume in another country. It may be necessary to take a population of transactions from countries with a certain percentage of service value of a global business unit. With that consideration, site-services transactions could be extracted using various reporting tools for the selection of all significant countries.
Another factor making analysis of when services are completed for purposes of service value recognition difficult is that available time to process data could be relatively limited. In addition, a recent trend in IT infrastructure management is toward shifting programs to focus on standardization of job architectures across all business units in a global IT enterprise. To accommodate standardization, additional services could be required, making determination of when services are completed more difficult.
Pricing consistency may be inferred from a verification of sales discounts across a large enterprise. This is true because discounts remain relatively constant even though pricing may be significantly different for different parts of the enterprise. A known method for verification of IT service sales discounts is based on transaction level checking through a manual process. Such a process is tedious, and has significant associated costs in time and labor. If the time window is limited to perform such a manual transaction-level checking process, the verification delay would cause delays in financial reporting as well as service value recognition deferment and cash flow interruption.
In addition, manual analysis may not be practical if a large number of transactions is involved. By way of example, the cost of analyzing about 100,000 transactions could exceed up to $750,000.00 and require a time period of about three months.
Statistical sampling methods have not been systematically studied and utilized for the purpose of determining sales discounts across a large enterprise. In terms of statistical sampling methods and development, the traditional methods would assume a Gaussian distribution for the discount amount and compute for the required minimum sample size, based on certain confidence coverage requirement such as the sample mean is within a certain prescribed range of the population mean. One problem here is the distributional assumption: the discount amounts in general would change significantly from one transaction record to the next, depending on the underlying business transaction nature, and a single Gaussian distribution assumption is likely to be invalid.
An alternative approach is to assume a probability distribution for the discount rate, which falls in the [0, 1] interval. This approach may not produce acceptable results if no known sample data exists. Without known sampling data, information derived regarding sample sizes from the standard or the relaxed distributional assumption and the corresponding sample size determination framework are not reliable. Another problem occurs when the transaction population is heavily heterogeneous, and a forced single distribution assumption would yield a large variability (for example, variance) in the distribution when estimated. This could result in a sample size range that is not practically useful. Also, a vague probability inference about the estimation may result.
If an IT enterprise cannot establish an acceptable pricing consistency for bundled combinations of services and hardware, service value recognition may be delayed. This could result in an undesirable delay in service value collection, which in turn could have a detrimental effect on cash flow. Moreover, an acceptable estimate of service rates (or equivalently, the discount rates from the standard pricing) must take into account the presence of all the aforementioned challenging factors, and arrive at an efficient pricing consistency checking method that can be accepted and used in a timely manner.